Top Tech Jobs & Startup Jobs

Hands-on Senior Site Reliability Engineer responsible for improving infrastructure tooling and automation, building and supporting core applications, monitoring capacity and performance, troubleshooting incidents, participating in on-call rotations, and collaborating across teams to scale a multi-cloud, multi-region content serving platform and advance observability and SLO frameworks.

Lead technical reliability initiatives across a multi-cloud, multi-region active-active content platform. Architect and evolve core services, observability and logging, automation and capacity planning. Mentor engineers, drive cross-team reliability projects, define standards (IaC, SLOs, on-call) and proactively improve platform scalability and incident outcomes.

Lead SRE to provide technical leadership for a multi-cloud, multi-region active-active content platform. Define automation and IaC strategy, design core platform and logging/observability systems, establish capacity and performance frameworks, lead cross-team reliability initiatives, improve on-call and incident response, mentor engineers, and proactively address systemic reliability and scaling challenges.

Deliver and maintain scalable, reliable full-stack solutions across front-end WYSIWYG designer experiences and back-end rendering systems. Write well-tested, reusable code, refactor to reduce technical debt, build tooling and processes, conduct empathetic code reviews, collaborate with stakeholders, mentor teammates, and drive projects from design through rollout.

Lead productionization of deep learning models and scale ML systems for marketing personalization. Build, optimize, monitor, and deploy reliable, high-performance AI products; integrate research into product, mentor engineers, and drive adoption and evaluation (A/B testing, monitoring, orchestration).

Own, build, and optimize Spark-based production data pipelines and ML data lake to support recommender systems and personalization. Scale cluster configurations on GCP Dataproc, ensure data quality and accessibility, support ML engineers for model development, resolve performance bottlenecks, and collaborate on platform architecture to deliver reliable data products that drive business value.